'''simple docstring'''




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       int:
    if "model" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''model.'''							,    ''''''							)
    if "norm1" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''norm1'''							,    '''attention.output.LayerNorm'''							)
    if "norm2" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''norm2'''							,    '''output.LayerNorm'''							)
    if "norm" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''norm'''							,    '''LayerNorm'''							)
    if "transformer" in orig_key:
        __lowerCamelCase            =    orig_key.split('''.'''							)[0].split('''_'''							)[-1]
        __lowerCamelCase            =    orig_key.replace(f"""transformer_{layer_num}"""							,    f"""encoder.layer.{layer_num}"""							)
    if "mha.attn" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''mha.attn'''							,    '''attention.self'''							)
    if "mha" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''mha'''							,    '''attention'''							)
    if "W_q" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''W_q'''							,    '''self.query'''							)
    if "W_k" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''W_k'''							,    '''self.key'''							)
    if "W_v" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''W_v'''							,    '''self.value'''							)
    if "ff1" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''ff1'''							,    '''intermediate.dense'''							)
    if "ff2" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''ff2'''							,    '''output.dense'''							)
    if "ff" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''ff'''							,    '''output.dense'''							)
    if "mlm_class" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''mlm.mlm_class'''							,    '''cls.predictions.decoder'''							)
    if "mlm" in orig_key:
        __lowerCamelCase            =    orig_key.replace('''mlm'''							,    '''cls.predictions.transform'''							)
    if "cls" not in orig_key:
        __lowerCamelCase            =    '''yoso.''' + orig_key

    return orig_key






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    for key in orig_state_dict.copy().keys():
        __lowerCamelCase            =    orig_state_dict.pop(UpperCamelCase__							)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __lowerCamelCase            =    val

    __lowerCamelCase            =    orig_state_dict['''cls.predictions.decoder.bias''']
    __lowerCamelCase            =    torch.arange(UpperCamelCase__							).expand((1, -1)							) + 2

    return orig_state_dict






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[str]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)['''model_state_dict''']
    __lowerCamelCase            =    YosoConfig.from_json_file(UpperCamelCase__							)
    __lowerCamelCase            =    YosoForMaskedLM(UpperCamelCase__							)

    __lowerCamelCase            =    convert_checkpoint_helper(config.max_position_embeddings							,    UpperCamelCase__							)

    print(model.load_state_dict(UpperCamelCase__							)							)
    model.eval()
    model.save_pretrained(UpperCamelCase__							)

    print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The json file for YOSO model config.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __UpperCAmelCase					=parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    __lowerCamelCase            =    0
    while len(UpperCamelCase__							) > 1:
        __lowerCamelCase            =    0
        # Consider two files with minimum cost to be merged
        for _ in range(2							):
            __lowerCamelCase            =    files.index(min(UpperCamelCase__							)							)
            temp += files[min_index]
            files.pop(UpperCamelCase__							)
        files.append(UpperCamelCase__							)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
  import doctest

  doctest.testmod()
